[Work Log]

July 21, 2013
Project Tulips
Subproject Data Association v2
Working path projects/​tulips/​trunk/​src/​matlab/​data_association_2
Unless otherwise noted, all filesystem paths are relative to the "Working path" named above.

Hypothesis: The likelihood covariance for the "virtual observations" scales linearly with the 2D likelihood variance.

Experiment: see experiments/exp_2013_07_21_likelihood_covariance.m. Constructs a likelihood using noise_variance of one and then scaling precisions afterward. Compares to directly-constructed likelihood precisions.

Results: Negligible difference

Conclusion: Practice matches theory--scaling likelihood precisions is equivalent to constructing likelihood with the scaled precision.

Discussion: This conclusion means that we can construct the likelihood precisions exactly once during training, and simply scale them as we modify the likelihood precision. It would make sense to always use 1.0 when computing precisions, and refactor all existing code to scale the matrix by the reciprocal of the noise variance before using it.


Posted by Kyle Simek
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